| language: | |
| - en | |
| - nl | |
| - fr | |
| - pt | |
| - it | |
| - es | |
| - de | |
| - da | |
| - pl | |
| - af | |
| - multilingual | |
| datasets: | |
| - jigsaw_toxicity_pred | |
| metrics: | |
| - F1 Accuracy | |
| pipeline_type: text-classification | |
| widget: | |
| - text: this is a lovely message | |
| example_title: Example 1 | |
| multi_class: false | |
| - text: you are an idiot and you and your family should go back to your country | |
| example_title: Example 2 | |
| multi_class: false | |
| # citizenlab/distilbert-base-multilingual-cased-toxicity | |
| This is multilingual Distil-Bert model sequence classifier trained based on [JIGSAW Toxic Comment Classification Challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) dataset. | |
| ## How to use it | |
| ```python | |
| from transformers import pipeline | |
| model_path = "citizenlab/distilbert-base-multilingual-cased-toxicity" | |
| toxicity_classifier = pipeline("text-classification", model=model_path, tokenizer=model_path) | |
| toxicity_classifier("this is a lovely message") | |
| > [{'label': 'not_toxic', 'score': 0.9954179525375366}] | |
| toxicity_classifier("you are an idiot and you and your family should go back to your country") | |
| > [{'label': 'toxic', 'score': 0.9948776960372925}] | |
| ``` | |
| ## Evaluation | |
| ### Accuracy | |
| ``` | |
| Accuracy Score = 0.9425 | |
| F1 Score (Micro) = 0.9450549450549449 | |
| F1 Score (Macro) = 0.8491432341169309 | |
| ``` |